Method and device for supporting an advanced driver assistance system in a motor vehicle

ABSTRACT

The invention relates to a method for supporting an advanced driver assistance system in a motor vehicle, comprising the following steps: providing a map, wherein categorized objects are stored in associated positions in the map, capturing environment data using at least one environment sensor system of the advanced driver assistance system, analyzing the captured environment data using an analysis apparatus of the advanced driver assistance system, wherein the captured environment data are analyzed for object recognition according to the categorized objects stored in the map. Furthermore, the invention relates to an associated device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/325,971, filed on Feb. 15, 2019 which claims priority to GermanApplication No. DE 10 2016 215 249.1, filed on Aug. 16, 2016 with theGerman Patent and Trademark Office. The contents of the aforesaidapplication are incorporated herein for all purposes.

The invention relates to a method and a device for supporting anadvanced driver assistance system in a motor vehicle.

BACKGROUND

Modern motor vehicles offer a plurality of advanced driver assistancesystems that support the driver in driving the motor vehicle. Suchadvanced driver assistance systems may include parking assist systems,lane departure warning or lane keeping systems, or navigationapparatuses, etc.

Operating advanced driver assistance systems typically involves using anenvironment sensor system to capture an environment of the motor vehicleand analyzing the environment data that are generated. In doing so, itis possible that the environment data are misinterpreted and incorrectconclusions are drawn based on the analysis, for example because objectsin the environment were not recognized correctly.

SUMMARY

Thus, a technical object exists of providing a method and a device forsupporting an advanced driver assistance system in a motor vehicle inwhich an analysis of captured environment data is improved.

The technical object is achieved according to the invention by a methodand a device according to the independent claims. Embodiments of theinvention are described in the dependent claims and in the followingdescription.

In one aspect, a method for supporting an driver assistance system in amotor vehicle is made available, which comprises the following steps:providing a map, wherein categorized objects are stored in associatedpositions in the map, capturing environment data using at least oneenvironment sensor system of the advanced driver assistance system,analyzing the captured environment data using an analysis apparatus ofthe advanced driver assistance system, wherein the captured environmentdata are analyzed for object recognition according to the categorizedobjects stored in the map.

In another aspect, a device for supporting a driver assistance system ina motor vehicle is provided, comprising a controller for processing amap that has been provided, wherein categorized objects are stored inassociated positions in the map, an advanced driver assistance systemwhich has at least one environment sensor system for capturingenvironment data and an analysis apparatus, wherein the analysisapparatus is designed such that the environment data captured areanalyzed for object recognition according to the categorized objectsstored in the map.

BRIEF DESCRIPTION OF THE DRAWINGS

In the FIGS.:

FIG. 1 shows a schematic representation of an embodiment of a device forsupporting an advanced driver assistance system in a motor vehicle;

FIG. 2 a shows a schematic representation of a typical road trafficscene at an intersection;

FIG. 2 b shows a schematic map associated with the scene represented inFIG. 2 a , in which categorized objects are stored;

FIG. 3 a shows a schematic representation of another typical roadtraffic scene on a country road at night;

FIG. 3 b shows a schematic map associated with the scene represented inFIG. 3 a , in which categorized objects are stored; and0

FIG. 4 shows a schematic flow chart of a method for supporting anadvanced driver assistance system in a motor vehicle.

DETAILED DESCRIPTION

One basic idea of the present teaching is to take into account alreadyexisting information, which is provided in a map in the form ofcategorized objects, for analyzing captured environment data. In thismanner, the performance of object recognition and object categorizationusing captured environment data can be improved. For this purpose,environment data are captured by at least one environment sensor systemof an advanced driver assistance system and analyzed in an analysisapparatus of the advanced driver assistance system. The analysisincludes recognition of objects from the captured environment data. Indoing so, the objects may particularly be categorized.

In some embodiments, the analysis is performed using pattern recognitionmethods. The pattern recognition methods determine features, forexample, that are characteristic of defined objects based on which theobject may be identified and/or categorized. By determining relativepositions of the objects in the environment of the motor vehicle, themotor vehicle may, e.g., locate its position on the map by comparing itagainst the categorized objects stored in the map and thus orient itselfwithin the environment.

The motor vehicle locates its position on the map for example bycomparing recognized objects and their relative positions within theenvironment of the motor vehicle against objects stored in the map. Forthis purpose, it may be provided in an embodiment that a roughlocalization is also performed by means of a global positioning system(e.g. GPS). Based on the position found, the controller will thendetermine e.g. objects that are stored in the map for the currentenvironment. The objects, their relative positions, and theircategorizations are then taken into account for the analysis.

One embodiment provides that a probability of existence of an objectthat is not unambiguously recognized in the captured environment data israised based on at least one categorized object stored in the map. Theprobability of existence here will describe the probability of existenceof a relevant object. Such a non-unambiguously recognized object may bea road marking, for example, that is partly covered with leaves or snow.In the map provided, this road marking is stored as a categorized object(or as several categorized objects), so that, even if the road markingis not unambiguously recognized in the environment data captured, it canbe concluded that the road marking is present in this position. With themethod described, the object recognition in the captured environmentdata thus becomes more robust.

Another embodiment provides that a probability of existence of an objectthat is recognized in the captured environment data is lowered based onat least one categorized object stored in the map. This is beneficialwhen it is to be avoided that the assistance system is triggered becauseof “phantom objects”, for example. Here, “phantom objects” shall beobjects that are misinterpreted by the advanced driver assistancesystem. Based on the categorized objects stored in the map, theplausibility of recognized objects can, however, be checked and,provided they are not relevant to the advanced driver assistance system,their respective probability of existence can be lowered. In an extremecase, such an object may even be completely rejected hereupon.

If the advanced driver assistance system receives environment data froma radar sensor system, for example, objects in the environment of themotor vehicle that appear to be obstacles from a greater distance butprove to not be an obstacle upon approach may be classified, using themethod described, as less relevant by lowering their probability ofexistence. For radar sensors, objects of this type are in particularlarge metallic objects such as drain covers or overhead gantry signsacross highways. If such “phantom objects” are stored in the map, theywill be recognized accordingly when they are captured and analyzedagain, and will be classified as less relevant or even rejected due tothe probability of existence being lowered during the analysis.

Another example associated with a high-beam assist system illustratesthe benefits offered by the method. Such a high-beam assist system(masked continuous high beam) is utilized during continuous high beamoperation in order to automatically switch the high beams to low beamswhen oncoming motor vehicles are captured and recognized. In doing so,static light sources such as street lights may have an unwanted effectif they are wrongly recognized as oncoming motor vehicles. The methoddescribed now makes it possible to avoid switching to low beams based oncategorized objects stored in the map for the street lights if thecontext makes it clear that the light sources captured are notheadlights of oncoming motor vehicles but are instead street lights.Then, a plausibility check is run for the recognized objects using thecategorized objects stored in the map for these positions and, providedthey are not relevant to the corresponding advanced driver assistancesystem, i.e. the high-beam assist system in this example, they arecategorized as less relevant or even as not relevant by means of a lowerprobability of existence. In the example, the high beams are thus notswitched to low beams. This represents a significant gain in convenienceand increases safety when driving a motor vehicle at night.

One embodiment provides that at least one parameter of the advanceddriver assistance system is configured according to at least onecategorized object stored in the map. In this manner, both theenvironment sensor system and the analysis apparatus can be adjusted tothe objects anticipated to occur in the environment.

In particular, one embodiment provides in this regard that the at leastone parameter indicates a resolution with which at least part of thecaptured environment data is analyzed. In this manner, it is possible toadjust the resolution of the analysis for specific regions or specificdirections or solid angles of the environment. Accordingly, regions ordirections of the environment in which specific categorized objects,e.g. objects that are of special relevance such as traffic lights ortraffic signs with important traffic guidance information, can beanalyzed with a higher resolution and thus in greater detail than otherregions in which no or fewer relevant objects are anticipated. In thismanner, it is possible to conserve computing power which is needed forthe analysis.

It may be provided in a corresponding embodiment that the environmentsensor system is a camera. The camera will then capture environment datain the form of images of the environment. In a captured image, it willthen be possible to analyze a specific region that corresponds with aspecific direction or a specific solid angle of the environment ingreater detail than other regions. If, for example, a traffic light or atraffic sign is anticipated in a specific direction or solid angle, theregion in the image corresponding with this direction or solid anglewill be analyzed with a higher resolution than the remaining regions.The computing effort for analyzing the environment data can hereby besignificantly reduced.

Another embodiment provides that an object that is stored in the map butwhich is not captured by the environment sensor system and/or is notrecognized by the analysis apparatus in the captured environment datawill be taken into account by the advanced driver assistance system. Inthis manner, objects that, either temporarily or due to an unfavorabledirection of capture or an unfavorable angle or solid angle region ofcapture, are not depicted in the environment data captured, can still betaken into account by the advanced driver assistance system.Accordingly, e.g. obstacles that are categorized and stored in the mapbut are not currently being captured, for example due to weatherconditions present at the time, can still be taken into account by theadvanced driver assistance system.

Another embodiment provides that the objects are or are beingcategorized according to at least one of the following categories: aprobability of existence, an object type, a sensor-dependent probabilityof capture, a traffic guidance relevance, a minimum resolution, and/or aweather dependency.

A probability of existence represents, e.g., a probability of theexistence of a specific object. Such a probability of existence may alsobe dependent upon, e.g., a time of day, of the week, or of the year. Forexample, particularly vegetation manifests more prominently in thesummer than in the winter, which causes objects corresponding with thevegetation to manifest differently or change over the course of theyear.

An object type particularly designates the type of the object capturedin general or in the context with respect to a specific advanced driverassistance system. Some non-exhaustive examples of object types are:traffic signs, vegetation, road markings, curbs, traffic lights,building facades, guard rails, guide posts, street lights, and citylimits signs.

A sensor-dependent probability of capture may, e.g., particularlydesignate the suitability of the object for being able to be captured bya specific sensor or a specific sensor type. Such a sensor-dependentprobability of capture may particularly be directionally dependent. Forexample, a radar sensor or a camera can capture a flat traffic sign verywell from the front but only very poorly from the side.

A traffic guidance relevance may, e.g., designate the suitability orcharacteristic of an object for serving as a traffic guidancecharacterization. Accordingly, road markings that characterize anddelimit individual lanes, for example, have a high traffic guidancerelevance, a building facade, on the other hand, has a low trafficguidance relevance. Likewise, a sign promoting sights of interest fortourists will have a lower traffic guidance relevance compared to aspeed limit sign.

A minimum resolution shall particularly characterize an object-dependentdetail size that must be captured by a sensor for the object to becorrectly recognized. In order to be able to recognize directionalinformation for different places on a traffic sign from a specificdistance, the letters and other characters printed on the traffic signmust be captured with a minimum resolution. Here, it may be particularlyprovided that the minimum resolution is derived depending on thesituation from the object-dependent detail size. If the detail size is,for example, 1 cm, a higher minimum resolution must be used if thedistance between the sensor system and the object is long than if thedistance is short. The minimum resolution can thus be calculated as afunction of the distance and the environment sensor system used.

A weather dependency designates a dependency between the object andweather conditions. If the environment sensor system is a camera, forexample, objects are more difficult to capture during heavy rain than insunny weather. In particular, the weather dependency may also beprovided according to the environment sensor system used.

One embodiment provides that the map is provided using a map creatorapparatus. Such a map creator apparatus may be formed e.g. onboard themotor vehicle. The map creator apparatus receives environment data froman environment sensor system, for example the same environment sensorsystem used by the advanced driver assistance system. Among theenvironment data received, the map creator apparatus recognizes objectsusing common pattern recognition methods. The recognized objects arecategorized and allocated to an associated position on the map. Such aposition may particularly be three-dimensional. The categorization(s) is(are) allocated to the object and the position of the object so thatthey can be retrieved after the map has been provided.

It may also be provided that the map creator apparatus is formed outsidethe motor vehicle in one embodiment. For example, the map may beprovided by a commercial or non-commercial service. The map will then becreated independently from the motor vehicle, e.g. using an area mappingvehicle dedicated to this purpose. The map created is then analyzed,revised, and, if applicable, augmented with additional environment data,objects, and categorizations.

The map created in this manner is then provided to the motor vehicle or,respectively, the device via a server, for example.

It may furthermore be provided that the created maps are exchangedand/or supplemented among different services or motor vehicles in someembodiments. In this manner, a current depiction of the physical worldcan be generated and provided at all times.

One embodiment provides that the map is updated based on the objectsrecognized in the environment data captured. For this purpose, therecognized objects are stored in the map with their categorizations and,where possible, older objects in the same positions are replaced withcurrent objects and/or categorizations. It must, however, be ensured inthis context that only if there is sufficient probability that a currentobject is truly located in the corresponding position and has acorresponding categorization should such an object replace the existingobject in the map. One criterion that would, for example, have to be metfor a replacement of the objects and/or the categorization to beimplemented, is that the resolution with which the current object is tobe captured is sufficiently high. Only if the resolution is sufficientlyhigh will the object and/or the categorization be replaced. If, however,the resolution is not sufficient, no update is made in the map for thecorresponding object and/or the categorization.

One embodiment provides that the environment sensor system is a camera.With the camera, visual images of the environment can be captured for aspecific solid angle. In the captured images, objects and theircategorizations can then be recognized using pattern recognitionmethods.

Further embodiments provide that the advanced driver assistance systemis a position finding apparatus or a navigation apparatus or a lanedeparture warning or lane keeping assistant or a parking assist systemor a lighting assistant or a traffic sign recognition apparatus.

Parts of the device may be formed individually or assembled as acombination of hardware and software, for example as programmed codethat is executed in a micro-controller or a microprocessor.

In the following, the invention will be explained in greater detail withreference to the drawings using further exemplary embodiments.

In FIG. 1 , a schematic representation of an embodiment of a device 1for supporting an advanced driver assistance system 4 in a motor vehicle50 is shown. The device 1 includes a controller 2, a memory 3, and anadvanced driver assistance system 4. The advanced driver assistancesystem 4 comprises an environment sensor system 5 and an analysisapparatus 6. The advanced driver assistance system 4 may for example bea position finding apparatus, a navigation apparatus, a lane departurewarning or lane keeping system, a parking assist system, a lightingassistant, a traffic sign recognition apparatus, or another suitableapparatus which supports the driver in driving the motor vehicle 50.

The environment sensor system 5, e.g. a camera, captures a currentenvironment of the motor vehicle 50 and feeds captured environment data8 to the analysis apparatus 6. These environment data 8 may for examplebe captured images of the environment. In the captured environment data8, the analysis apparatus 6 recognizes objects using pattern recognitionmethods and categorizes these objects.

A map 7 in which categorized objects are stored in associated positionsis stored in the memory 3. In general, the motor vehicle 50 is able tofind its position within an environment based on the categorized objectsstored in the map 7 and their associated positions.

The map 7 may for example be provided via a map creator apparatus 9provided for this purpose. The map creator apparatus 9 has created themap e.g. on the basis of previously captured environment data or hasbeen provided with them via an interface and has stored the map 7 in thememory 3 for later retrieval.

It is assumed, here, that the motor vehicle 50 has already found itsposition within the environment, for example using an apparatus designedfor this purpose taking into account the map 7, or using a globalpositioning system (e.g. GPS). Only after this initial positioning willthe controller 2 retrieve the stored categorized objects for the currentenvironment from the map 7.

The controller 2 retrieves the map 7 stored in the memory 3, at leastfor the current environment, and provides this retrieved map to theadvanced driver assistance system 4. The analysis apparatus 6 of theadvanced driver assistance system 4 is designed such that the capturedenvironment data 8 are analyzed for object recognition according to thecategorized objects stored in the map 7.

Such an analysis may provide e.g. that a probability of existence of anobject that is not unambiguously recognized in the captured environmentdata 8 is raised based on at least one categorized object stored in themap 7. Furthermore, it may also be provided that an object recognized inthe captured environment data 8 is evaluated as less relevant or, inextreme cases, even rejected completely as a non-relevant object, basedon at least one categorized object stored in the map 7, by lowering itsprobability of existence. In addition, it may be provided that at leastone parameter of the advanced driver assistance system 4 is configuredaccording to at least one categorized object stored in the map 7.Particularly, it may be provided that this at least one parameterindicates a resolution with which at least part of the capturedenvironment data 8 is being analyzed.

It may furthermore be provided that the analysis apparatus 6 of theadvanced driver assistance system 4 is designed such that an objectstored in the map 7 that is not captured by the environment sensorsystem 5 and/or that is not recognized by the analysis apparatus 6 inthe captured environment data 8 is taken into account anyway.

The objects stored in the map 7 may for example be categorized accordingto the following categories: a probability of existence, an object type,a sensor-dependent probability of capture, a traffic guidance relevance,a minimum resolution, and/or a weather dependency.

It may be provided that the map 7 is updated based on the objectsrecognized in the captured environment data 8 by the analysis apparatus6. It must, however, be ensured in this context that only if there issufficient probability that an object recognized at the time is trulylocated in the corresponding position on the map 7 and has acorresponding categorization should such an object replace the existingobject in the map 7. One criterion that would, for example, have to bemet for a replacement of the objects and/or the categorization to beimplemented, is for example that the resolution with which the currentobject is to be captured is sufficiently high.

It may furthermore be provided that at least one parameter 19 of theadvanced driver assistance system 4 is configured according to at leastone categorized object stored in the map 7. This may for example be aresolution with which the environment sensor system 5 captures theenvironment or with which the analysis apparatus 6 analyzes the capturedenvironment data 8.

FIG. 2 a shows a schematic representation of a typical road trafficscene 10 at an intersection 11 for illustrating the method. FIG. 2 bshows a schematic map 7 associated with the scene 10 represented in FIG.2 a , in which categorized objects 20-1, 20-2, 20-3 are stored.

The schematic scene 10 represented in FIG. 2 a corresponds for examplewith an image captured by a camera of a motor vehicle at the moment whenthe vehicle approaches the intersection 11. In the schematicallyrepresented scene 10, the intersection 11 with multiple lanes 12,multiple traffic lights 13-1, 13-2, and exemplary road markings 15 canbe seen. The schematically represented scene 10 represents environmentdata captured by the camera. These captured environment data areanalyzed by the analysis apparatus of the advanced driver assistancesystem according to the categorized objects 20-1, 20-2, 20-3 stored inthe map 7.

For the traffic lights 13-1, 13-2, for example, the objects 20-1 and20-2 are stored in the map 7. These objects 20-1 and 20-2 have thecategorizations 21-1, 21-2. The categorizations 21-1, 21-2 may e.g.indicate that the objects 20-1, 20-2 are traffic lights. Here, adirectional dependency in particular may also be provided. Accordingly,the categorizations 21-1, 21-2 may for example provide that the trafficlights 13-1, 13-2 can only be captured and/or recognized in thedirection of the motor vehicle approaching the intersection 11 from thedirection represented. In particular, it may now be provided that atleast one parameter of the advanced driver assistance system isconfigured based on the categorized objects 20-1, 20-2 stored in the map7. For example, such a parameter may indicate a minimum resolution withwhich specific objects or regions or solid angle regions in the capturedenvironment data must be analyzed. It may e.g. be provided that theregions 14-1, 14-2 marked in FIG. 2 a , which include the traffic lights13-1, 13-2 in a captured image, are captured and/or analyzed with ahigher resolution. The remaining regions, which are not part of theregions 14-1, 14-2, are captured and/or analyzed accordingly with alower resolution. Thereby, computing power can be conserved during theanalysis.

It is possible that an object 20-3 stored in the map 7 is not recognizedunambiguously in the captured environment data. This may be, forexample, the road marking 15, on which leaves 16 are present. The leaves16 result in the environment sensor system not being able to capture theroad marking 15 completely and coherently and the analysis apparatustherefore not being able to recognize the road marking 15 unambiguously.It is then provided, here, that the road marking 15, which was notrecognized unambiguously, or the associated object 20-3, which was notrecognized unambiguously, respectively, is evaluated as a relevantobject based on the categorized object 20-3 stored in the map 7 for theroad marking 15. Although the road marking 15 was not recognizedunambiguously, a corresponding object can thus be taken into account bythe advanced driver assistance system based on the categorized object20-3 stored in the map 7. If the advanced driver assistance system ise.g. a lane departure warning or lane keeping assistant, the lanedeparture warning or lane keeping assistant can take the road marking 15into account for keeping the lane despite it being covered with theleaves 16.

To further illustrate the embodiments, FIG. 3 a shows a schematicrepresentation of another typical road traffic scene 10 on a countryroad 17. Corresponding to the scene 10 represented in FIG. 3 a , anassociated schematic map 7 is shown in FIG. 3 a , in which categorizedobjects 20-1, 20-2, 20-3, 20-4 are stored. The country road 17 is linedwith street lights 18.

If the advanced driver assistance system is, for example, a high-beamassist system (masked continuous high beam), a wrongly executed switchto low beams can be avoided based on the categorized objects 20-1, 20-2,20-3, 20-4 stored in the map 7, which are associated with each of thestreet lights 18. A motor vehicle that is, for example, located in thelane 12 captures the light cones of the street lights 18 using itsenvironment sensor system. It is then possible that the captured lightcones of the street lights 18 are wrongly recognized by the analysisapparatus as oncoming motor vehicles. The method described, however,achieves that the advanced driver assistance system analyzes thecaptured environment data for object recognition according to thecategorized objects 20-1, 20-2, 20-3, 20-4 stored in the map 7.

The categorizations 21-1, 21-2, 21-3, 21-4 allocated to the objects20-1, 20-2, 20-3, 20-4 stored in the map 7 may, for example, indicatethat they are street lights 18 and thus static light objects. Due tothis pre-existing information, the analysis apparatus can reject theobjects recognized in the captured environment data, i.e. in this casethe light cones of the individual street lights 18, as non-relevantobjects based on the categorized objects 20-1, 20-2, 20-3, 20-4 storedin the map 7.

This results in the high-beam assist system not switching the high beamsto low beams. Thereby, convenience and safety are increased for themotor vehicle, such that an unnecessary reduction of the illumination isavoided.

FIG. 4 shows a schematic flow chart of a method for supporting anadvanced driver assistance system in a motor vehicle. After the start100 of the method, a map with categorized objects stored therein isprovided in a first method step 101. The provision may for example beperformed using a map creator apparatus. Alternatively, the map may alsobe stored in a memory.

In the subsequent method step 102, environment data of an environment ofthe motor vehicle are captured with at least one environment sensorsystem of an advanced driver assistance system of a motor vehicle. Suchan environment sensor system may e.g. be a camera that captures imagesof the environment of the motor vehicle.

It is assumed, here, that the motor vehicle has already found itsposition within the environment, for example using an apparatus designedfor this purpose, taking into account the map or using a globalpositioning system (e.g. GPS). Only after this initial position findingwill the controller 2 retrieve the stored categorized objects for thecurrent environment from the map 7.

Subsequently, the captured environment data are analyzed in the methodstep 103 using an analysis apparatus of the advanced driver assistancesystem, wherein the captured environment data are analyzed for objectrecognition according to the categorized objects stored in the mapprovided.

Hereby, it may be provided for example in method step 104 that aprobability of existence of an object in the captured environment datathat is not unambiguously recognized is raised based on at least onecategorized object stored in the map.

Additionally or alternatively, method step 105 may provide that aprobability of existence of an object in the captured environment datathat is recognized is lowered based on at least one categorized objectstored in the map. Via a corresponding reduction of the probability ofexistence, an object may even be completely rejected.

Additionally or alternatively, method step 106 may provide that at leastone parameter of the advanced driver assistance system is configuredaccording to at least one categorized object stored in the map.

Method step 107 may furthermore provide that an object that is stored inthe map but which is not captured by the environment sensor systemand/or is not recognized by the analysis apparatus in the capturedenvironment data will still be taken into account by the advanced driverassistance system.

A final method step 108 may provide that the map is updated based on theobjects recognized in the captured environment data. It must, however,be ensured in this context that only if there is sufficient probabilitythat a current object is truly located in the corresponding position andhas an appropriate categorization should such an object replace theexisting object in the map. One criterion that would, for example, haveto be met for a replacement of the objects and/or the categorization tobe implemented, is for example that the resolution with which thecurrent object is to be captured is sufficiently high. Then, the methodis at an end 108.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments.

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practicing the claimedinvention, from a study of the drawings, the disclosure, and theappended claims. In the claims, the word “comprising” does not excludeother elements or steps, and the indefinite article “a” or “an” does notexclude a plurality. A single processor, module or other unit or devicemay fulfil the functions of several items recited in the claims.

The mere fact that certain measures are recited in mutually differentdependent claims or embodiments does not indicate that a combination ofthese measured cannot be used to advantage. Any reference signs in theclaims should not be construed as limiting the scope.

REFERENCE LIST

-   1 Device-   2 Controller-   3 Memory-   4 Advanced driver assistance system-   5 Environment sensor system-   6 Analysis apparatus-   7 Map-   8 Environment data-   9 Map creator apparatus-   10 Scene-   11 Intersection-   12 Lane-   13-1 Traffic light-   13-2 Traffic light-   14-1 Region-   14-2 Region-   15 Road marking-   16 Leaves-   17 Country road-   18 Street light-   19 Parameter-   20-1 Object-   20-2 Object-   20-3 Object-   20-4 Object-   21-1 Categorization-   21-2 Categorization-   21-3 Categorization-   50 Motor vehicle

That invention claimed is:
 1. A method for supporting an advanced driverassistance system in a motor vehicle, comprising the steps of:determining a position of the motor vehicle; providing a map of thedetermined position, wherein categorized objects are stored inassociated positions in the map; capturing environment data of thedetermined position using at least one environment sensor system of theadvanced driver assistance system; analyzing the captured environmentdata using an analysis apparatus of the advanced driver assistancesystem, wherein the captured environment data are analyzed for objectrecognition according to the categorized objects stored in the map,wherein a probability of existence of an object in the capturedenvironment data is evaluated based on at least one categorized objectstored in the map.
 2. The method according to claim 1, wherein theevaluated probability of existence of an object that is notunambiguously recognized in the captured environment data is raisedbased on at least one categorized object stored in the map.
 3. Themethod according to claim 1, wherein the evaluated probability ofexistence of an object that is recognized in the captured environmentdata is lowered based on at least one categorized object stored in themap.
 4. The method according to claim 1, wherein at least one parameterof the advanced driver assistance system is configured according to atleast one categorized object stored in the map.
 5. The method accordingto claim 1, wherein the at least one parameter indicates a resolutionwith which at least part of the captured environment data is analyzed.6. The method according to claim 1, wherein an object that is stored inthe map, but which is not captured by the environment sensor system andis not recognized by the analysis apparatus in the captured environmentdata is taken into account by the advanced driver assistance system. 7.The method according to claim 1, wherein the objects are categorizedaccording to at least one of the following categories: a probability ofexistence, an object type, a sensor-dependent probability ofacquisition, a traffic guidance relevance, a minimum resolution, and aweather dependency.
 8. The method according to claim 1, wherein the mapis provided by a map creator apparatus.
 9. The method according to claim1, wherein the map is updated based on the objects recognized in thecaptured environment data.
 10. A device for a motor vehicle, comprising:a device that determines a position of the motor vehicle; an environmentsensor system that captures current environment data of the determinedposition; a memory that stores a map of the determined position, whereincategorized objects are stored in associated positions in the map; acontroller that determines a position of the motor vehicle on the map,correlates categorized objects from the map with the capturedenvironment data, conducts object recognition on the capturedenvironment data using the correlated categorized objects from the map,and evaluates a probability of existence of an object in the capturedenvironment data based on at least one categorized object stored in themap.
 11. The device according to claim 10, wherein the evaluatedprobability of existence of an object that is not unambiguouslyrecognized in the captured environment data is raised based on at leastone categorized object stored in the map.
 12. The device according toclaim 10, wherein the evaluated probability of existence of an objectthat is recognized in the captured environment data is lowered based onat least one categorized object stored in the map.
 13. The deviceaccording to claim 10, further comprising an advanced driver assistancesystem, wherein at least one parameter of the advanced driver assistancesystem is configured according to at least one categorized object storedin the map.
 14. The device according to claim 13, wherein the at leastone parameter indicates a resolution with which at least part of thecaptured environment data is analyzed.
 15. The device according to claim13, wherein an object that is stored in the map, but which is notcaptured by the environment sensor system and is not recognized by theanalysis apparatus in the captured environment data is taken intoaccount by the advanced driver assistance system.
 16. The deviceaccording to claim 10, wherein the objects are categorized according toat least one of the following categories: a probability of existence, anobject type, a sensor-dependent probability of acquisition, a trafficguidance relevance, a minimum resolution, and a weather dependency. 17.The device according to claim 10, wherein the map is provided by a mapcreator apparatus.
 18. The device according to claim 10, wherein the mapis updated based on the objects recognized in the captured environmentdata.
 19. A device for a motor vehicle, comprising: a global positioningsystem (GPS) device that determines a position of the motor vehicle; anenvironment sensor system that captures environment data of thedetermined position; a data processing system; a non-transitorycomputer-readable storage device storing data for access by anapplication executed on the data processing system, the storage devicecomprising a map of the determined position, wherein categorized objectsare stored in associated positions in the map; and an advanced driverassistance application stored in the non-transitory computer-readablestorage device and executable by the data processing system to:determine a position of the motor vehicle on the map, correlatecategorized objects from the map with the captured environment data,conduct object recognition on the captured environment data using thecorrelated categorized objects from the map, and evaluate a probabilityof existence of an object in the captured environment data based on atleast one categorized object stored in the map.